The Role of Artificial Intelligence in Strategic Disclosure of Financial Performance

Abstract

The purpose of this research is to demonstrate how artificial intelligence contributes to strategic disclosure by predicting financial performance evaluation using published financial statement data related to the performance of banks listed on the Iraq Stock Exchange for the period from 2015 to 2024.Specifically, this impact is reflected in increased confidence among accounting information users in assessing future financial performance when making decisions. This is due to AI technology's ability to process and analyze data quickly and accurately. It can search for and classify patterns and financial data in an unprecedented manner, unlike traditional methods, which lack automatic data processing to achieve optimal results. To leverage AI technology, Iraqi companies still lack the technology to improve their financial reporting and the information they disclose to beneficiaries and stakeholders, particularly regarding the company's plans, strategic objectives, and future performance forecasts. Accordingly, the researcher used artificial intelligence, represented by a neural network model, to predict the financial performance of the sample companies for the period from 2025 to 2029. This requires measuring indicators to evaluate the financial performance of these banks (profitability ratio, liquidity ratio, debt management ratio, and asset management ratio). The researcher then proposed a strategic disclosure model that explains the prediction of future information on financial statements, which was extracted based on the financial indicators of the sample banks. The most important findings of the research are:

 Artificial intelligence applications are beneficial to the banking sector, as they provide future information useful to both investors and stakeholders, in addition to increasing efficiency in the financial sector, which achieves economic benefits. In particular, the results predicted using the neural network application are characterized by accuracy and transparency, and are close to actual values when compared to other traditional forecasting methods.